Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Abstract

Background

There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.

Methods and findings

A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong’s test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855–0.866) on the joint MSH–NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU ( P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927–0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745–0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH–NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data ( P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system–specific biases.

Conclusion

Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

Abstract

Eric Oermann and colleagues ask whether a DL-based model for pneumonia detection performs well in external validation and consider the effects of hospital system–specific biases.

Author summary

Why was this study done?

• Early results in using convolutional neural networks (CNNs) on X-rays to diagnose disease have been promising, but it has not yet been shown that models trained on X-rays from one hospital or one group of hospitals will work equally well at different hospitals.

• Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems.

What did the researchers do and find?

• A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest X-rays from the National Institutes of Health Clinical Center (NIH; n = 112,120 from 30,805 patients), Mount Sinai Hospital (42,396 from 12,904 patients), and Indiana University Network for Patient Care ( n = 3,807 from 3,683 patients).

• In 3 out of 5 natural comparisons, performance on chest X-rays from outside hospitals was significantly lower than on held-out X-rays from the original hospital system.

• CNNs were able to detect where a radiograph was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly.

What do these findings mean?

• The performance of CNNs in diagnosing diseases on X-rays may reflect not only their ability to identify disease-specific imaging findings on X-rays but also their ability to exploit confounding information.

• Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance.